class_docstr: Callback that prints metrics to stdout.
  
  Arguments:
      count_mode: One of "steps" or "samples".
          Whether the progress bar should
          count samples seen or steps (batches) seen.
      stateful_metrics: Iterable of string names of metrics that
          should *not* be averaged over an epoch.
          Metrics in this list will be logged as-is.
          All others will be averaged over time (e.g. loss, etc).
          If not provided, defaults to the `Model`'s metrics.
  
  Raises:
      ValueError: In case of invalid `count_mode`.
docstr: Updates the progbar.
docstr_tok: ["Updates","the","progbar","."]
code: def _batch_update_progbar(self, logs=None):
      """Updates the progbar."""
      if self.stateful_metrics is None:
          if logs:
              # self.stateful_metrics = set(m.name for m in self.model.metrics)
              self.stateful_metrics = set(logs.keys())
          else:
              self.stateful_metrics = set()
  
      logs = copy.copy(logs) if logs else {}
      batch_size = logs.pop("size", 0)
      num_steps = logs.pop(
          "num_steps", 1
      )  # DistStrat can run >1 steps.
      logs.pop("batch", None)
      add_seen = num_steps if self.use_steps else num_steps * batch_size
      self.seen += add_seen
      self.progbar.update(self.seen, list(logs.items()), finalize=False)
  
imports: import copy
  import os
  import sys
  import time
  import numpy
  from .callback import Callback
label_desc: Use image node_id 14 for calling the ProgbarLogger obj's underlying member method code with example usage: obj._batch_update_progbar(logs) without return types